Medication Therapy Management for Texas MediCAID Patients With Asthma and Chronic Obstructive Pulmonary Disease—A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Pharmacists can play an important role in providing medication therapy management (MTM) services, which focus on appropriate medication use. This pilot study aimed to describe pharmacists' MTM service provision, results/outcomes of pharmacists' recommendations and resolution/acceptance rate among patients with high-risk asthma and/or chronic obstructive pulmonary disease (COPD). METHODS: This was a prospective descriptive study of MTM services provided by community pharmacists to Texas Medicaid patients (5-63 years) with "high risk" asthma or COPD. Patients received in-person and telephone consultations that included medication review, asthma control test assessment, and education on adherence and proper medication/device use. Data extracted from MTM software was used to describe: reasons for MTM services, type of pharmacists' interventions, outcomes of pharmacists' recommendations and acceptance rate. RESULTS: Twenty-eight pharmacists provided 139 MTM interventions with 63 patients (2.2 interventions per patient). The most frequent intervention reason was complex drug therapy (53.2%), underuse of medication (8.6%), need for drug therapy (8.6%), new or changed prescription therapy (6.5%), and administration technique (5.0%). The resolution rate was 77.7%. Patient and prescriber, respectively, refused recommendation in 12% and 6% of the interventions. Outcomes included comprehensive medication review (46.7%), improved adherence (6.5%), therapeutic success (6.5%), improved administration technique (5.0%), and initiation of new therapy (5.0%). CONCLUSION: Through the provision of MTM, pharmacists were able to identify and intervene with medication-related problems. These interventions are instrumental in helping patients better manage their asthma/COPD. The high resolution rate was encouraging. Larger scale studies are needed to assess clinical and economic outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".